“Researchers have made new progress in the field of clustering analysis to address the issue of traditional fuzzy C-means (FCM) algorithm being susceptible to interference from factors such as noise and outliers during the clustering process. They proposed a fuzzy C-means clustering algorithm based on adaptive neighbor information, which enhances the algorithm's perception of data structures by introducing neighbor information of sample points and class center points, thereby improving the stability and performance of clustering. This innovative method not only enriches the theoretical system of cluster analysis, but also provides an effective tool for processing complex data in practical applications. The experimental results on the benchmark dataset show that this algorithm has improved performance by more than 10% compared to other advanced clustering algorithms. At the same time, the researchers also conducted a comprehensive evaluation of the algorithm from the aspects of parameter sensitivity, convergence, and ablation experiments, further verifying its feasibility and effectiveness. This research achievement is of great significance for promoting the development of clustering analysis field.”